docs: def make_benchark(n_train, n_test, n_dim=2):
      """ Compute the benchmarks for Ordianry Kriging 
  
      Parameters
      ----------
      n_train : int
        number of points in the training set
      n_test : int
        number of points in the test set
      n_dim : int
        number of dimensions (default=2)
  
      Returns
      -------
      res : dict
        a dictionary with the timing results
      """
      X_train = np.random.rand(n_train, n_dim)
      y_train = np.random.rand(n_train)
      X_test = np.random.rand(n_test, n_dim)
  
      res = {}
  
      for variogram_model in VARIOGRAM_MODELS:
          tic = time()
          OK = OrdinaryKriging(X_train[:, 0], X_train[:, 1], y_train,
                               variogram_model='linear',
                               verbose=False, enable_plotting=False)
          res['t_train_{}'.format(variogram_model)] = time() - tic
  
      # All the following tests are performed with the linear variogram model
      for backend in BACKENDS:
          for n_closest_points in N_MOVING_WINDOW:
  
              if backend == 'vectorized' and n_closest_points is not None:
                  continue  # this is not supported
  
              tic = time()
              OK.execute('points', X_test[:, 0], X_test[:, 1],
                         backend=backend,
                         n_closest_points=n_closest_points)
              res['t_test_{}_{}'.format(backend, n_closest_points)] = time() - tic
  
      return res